code: def logistic(
      random_state,
      loc=0.0,
      scale=1.0,
      size=None,
      chunk_size=None,
      gpu=None,
      dtype=None,
  ):
      r"""
      Draw samples from a logistic distribution.
  
      Samples are drawn from a logistic distribution with specified
      parameters, loc (location or mean, also median), and scale (>0).
  
      Parameters
      ----------
      loc : float or array_like of floats, optional
          Parameter of the distribution. Default is 0.
      scale : float or array_like of floats, optional
          Parameter of the distribution. Should be greater than zero.
          Default is 1.
      size : int or tuple of ints, optional
          Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
          ``m * n * k`` samples are drawn.  If size is ``None`` (default),
          a single value is returned if ``loc`` and ``scale`` are both scalars.
          Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.
      chunk_size : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      dtype : data-type, optional
        Data-type of the returned tensor.
  
      Returns
      -------
      out : Tensor or scalar
          Drawn samples from the parameterized logistic distribution.
  
      See Also
      --------
      scipy.stats.logistic : probability density function, distribution or
          cumulative density function, etc.
  
      Notes
      -----
      The probability density for the Logistic distribution is
  
      .. math:: P(x) = P(x) = \frac{e^{-(x-\mu)/s}}{s(1+e^{-(x-\mu)/s})^2},
  
      where :math:`\mu` = location and :math:`s` = scale.
  
      The Logistic distribution is used in Extreme Value problems where it
      can act as a mixture of Gumbel distributions, in Epidemiology, and by
      the World Chess Federation (FIDE) where it is used in the Elo ranking
      system, assuming the performance of each player is a logistically
      distributed random variable.
  
      References
      ----------
      .. [1] Reiss, R.-D. and Thomas M. (2001), "Statistical Analysis of
             Extreme Values, from Insurance, Finance, Hydrology and Other
             Fields," Birkhauser Verlag, Basel, pp 132-133.
      .. [2] Weisstein, Eric W. "Logistic Distribution." From
             MathWorld--A Wolfram Web Resource.
             http://mathworld.wolfram.com/LogisticDistribution.html
      .. [3] Wikipedia, "Logistic-distribution",
             http://en.wikipedia.org/wiki/Logistic_distribution
  
      Examples
      --------
      Draw samples from the distribution:
  
      >>> import mars.tensor as mt
      >>> import matplotlib.pyplot as plt
  
      >>> loc, scale = 10, 1
      >>> s = mt.random.logistic(loc, scale, 10000)
      >>> count, bins, ignored = plt.hist(s.execute(), bins=50)
  
      #   plot against distribution
  
      >>> def logist(x, loc, scale):
      ...     return mt.exp((loc-x)/scale)/(scale*(1+mt.exp((loc-x)/scale))**2)
      >>> plt.plot(bins, logist(bins, loc, scale).execute()*count.max()/\
      ... logist(bins, loc, scale).max().execute())
      >>> plt.show()
      """
      if dtype is None:
          dtype = (
              np.random.RandomState()
              .logistic(
                  handle_array(loc), handle_array(scale), size=(0,)
              )
              .dtype
          )
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorLogistic(seed=seed, size=size, gpu=gpu, dtype=dtype)
      return op(loc, scale, chunk_size=chunk_size)
  
file_path: mars/mars/tensor/random/logistic.py
imports: import numpy
  from ...None import opcodes
  from ...serialization.serializables import AnyField
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, handle_array, TensorDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: logistic(random_state, loc, scale, size, chunk_size, gpu, dtype) and returns: op